A survey of content-based image retrieval with high-level semantics
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The performance of a content-based image retrieval system mainly depends on the selected image features.
“The performance of a CBIR system mainly depends on these selected features [7].”
From Medical image retrieval using deep convolutional neural network (2017) · cites this paper as [7]
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Extracting texture features from arbitrary-shaped regions for image retrieval
Liu, Y., Zhou, X., & Ma, W. (2005) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2004.1394628 · Cited by 23 · see citing works
ONTOLOGIES FOR OBJECT-BASED IMAGE RETRIEVAL
Mezaris, V., Kompatsiaris, I. Y., & Strintzis, M. G. (2003) · doi:10.1142/9789812704337_0018 · Cited by 23 · see citing works
Content-based similarity retrieval of images based on spatial color distributions
Yamamoto, H., Iwasa, H., Yokoya, N., & Takemura, H. (2003) · International Conference on Image Analysis and Processing · doi:10.1109/iciap.1999.797718 · Cited by 20 · see citing works
SCALE AND ORIENTATION-INVARIANT TEXTURE MATCHING FOR IMAGE RETRIEVAL
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Texture Analysis in Machine Vision
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Characterizing the high-level content of natural images using lexical basis functions
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Some Formal Analysis of Rocchio's Similarity-Based Relevance Feedback Algorithm
Chen, Z., & Zhu, B. (2002) · Information Retrieval · doi:10.1023/a:1012730924277 · Cited by 16 · see citing works
A curve evolution approach for image segmentation using adaptive flows
Feng, H., Castañón, D. A., & Karl, W. C. (2002) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937666 · Cited by 16 · see citing works
IMAGE RETRIEVAL USING SPATIAL CONTEXT
Ren, W., Singh, M., & Singh, S. K. (2002) · doi:10.1142/9789812776266_0004 · Cited by 12 · see citing works
Triangle-inequality-based pruning algorithms with triangle tries
Berman, A. P., & Shapiro, L. G. (1998) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.333855 · Cited by 12 · see citing works
Incorporate discriminant analysis with EM algorithm in image retrieval
Tian, Q. C., Wu, Y., & Huang, T. S. (2002) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2000.869600 · Cited by 12 · see citing works
Colour-based relevance feedback for image retrieval
Low, W.-C., & Chua, T. (2002) · doi:10.1109/mmdbms.1998.709521 · Cited by 11 · see citing works
Learning image similarities and categories from content analysis and relevance feedback
Yang, Z., & Kuo, C. ‐. J. (2000) · doi:10.1145/357744.357927 · Cited by 10 · see citing works
Some Formal Analysis of Roccio's Similarity-Based Relvance Feedback Algorithm
Chen, Z., & Zhu, B. (2000) · International Symposium on Algorithms and Computation · Cited by 9 · see citing works
Spectrally Layered Color Indexing
Qiu, G., & Lam, K. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45479-9_11 · Cited by 9 · see citing works
Central Object Extraction for Object-Based Retrieval.
Kim, S., Park, S.-Y., & Kim, M. H. (2003) · Conference on Image and Video Retrieval · Cited by 5 · see citing works
Texture Retrieval with Linguistic Descriptions
Chiu, C., Lin, H.-C., & Yang, S.-N. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45453-5_40 · Cited by 5 · see citing works
On the Complexity of Rocchio’s Similarity-Based Relevance Feedback Algorithm
Chen, Z., & Fu, B. (2005) · Lecture notes in computer science · doi:10.1007/11602613_23 · Cited by 3 · see citing works
Object class recognition using images of abstract regions
Li, Y., Bilmes, J. A., & Shapiro, L. G. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1334000 · Cited by 3 · see citing works
The design of end-to-end optimal image retrieval systems
Vasconcelos, N. M. (2003) · Cited by 2 · see citing works
Flagged first, then by citations
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Sorted by citations
What Do Different Evaluation Metrics Tell Us About Saliency Models?
Bylinskii, Z., Judd, T., Oliva, A., Torralba, A. B., & Durand, F. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2815601 · Cited by 835 · see citing works
Processing Social Media Messages in Mass Emergency: A Survey
Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015) · ACM Computing Surveys · doi:10.1145/2771588 · Cited by 804 · see citing works
“In addition, there are cases of websites being set up in response to crises in 2003 [Palen and Liu 2007].”
BackgroundLocal Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval
Murala, S., Maheshwari, R. P., & Raman, B. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2188809 · Cited by 680 · see citing works
“A comprehensive and extensive literature survey on CBIR is presented in [1]–[4].”
BackgroundPatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval
Zhou, W., Newsam, S., Li, C., & Shao, Z. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.01.004 · Cited by 571 · see citing works
Understanding Deep Learning Techniques for Image Segmentation
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“Image segmentation is useful in many cases as they are representative of spatial relations among various objects [12, 127].”
BackgroundA review on automatic image annotation techniques
Zhang, D., Islam, M. M., & Lu, G. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.05.013 · Cited by 483 · see citing works
Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval
Xu, X., Shen, F., Yang, Y., Shen, H. T., & Li, X. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2676345 · Cited by 458 · see citing works
Medical image retrieval using deep convolutional neural network
Qayyum, A., Anwar, S. M., Awais, M., & Majid, M. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.05.025 · Cited by 433 · see citing works
“The performance of a CBIR system mainly depends on these selected features [7].”
BackgroundDeep multiple instance learning for image classification and auto-annotation
Wu, J., Yu, Y., Huang, C., & Yu, K. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298968 · Cited by 396 · see citing works
“Image annotation aims at producing rich image descriptions at different levels [26].”
BackgroundContent‐Based Image Retrieval and Feature Extraction: A Comprehensive Review
Latif, A., Rasheed, A., Sajid, U., Ahmed, J., Ali, N., Ratyal, N. I., Zafar, B., Dar, S. H., Sajid, M., & Khalil, T. (2019) · Mathematical Problems in Engineering · doi:10.1155/2019/9658350 · Cited by 348 · see citing works
“)e image feature vector can be used as an input for machine learning algorithms through training and test models and it can improve the performance of CBIR [1, 2].”
MethodGenerating Semantically Precise Scene Graphs from Textual Descriptions for Improved Image Retrieval
Schuster, S., Krishna, R., Chang, A., Fei-Fei, L., & Manning, C. D. (2015) · Workshop on Vision and Language (VL) · doi:10.18653/v1/w15-2812 · Cited by 336 · see citing works
“Rui et al. (1999), Liu et al. (2007), and Siddiquie et al. (2011) provide overviews of the developments of this field over the last twenty years.”
BackgroundImage retrieval based on micro-structure descriptor
Liu, G., Li, Z., Lei Zhang, A., & Xu, Y. D. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.02.003 · Cited by 323 · see citing works
“Content-based image retrieval (CBIR) was then emerging in 1980s [1].”
BackgroundImage retrieval based on multi-texton histogram
Liu, G., Lei Zhang, A., Hou, Y., Li, Z., & Yang, J.-Y. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.02.012 · Cited by 291 · see citing works
“…retrieval based on multi-texton histogram Guang-Hai Liu a, , Lei Zhang b, Ying-Kun Hou d, Zuo-Yong Li c, Jing-Yu Yang c a College of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, China b Department of Computing, The Hong Kong Polytechnic…”
MethodInfluentialSemantic content-based image retrieval: A comprehensive study
Alzu’bi, A., Amira, A., & Ramzan, N. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.07.012 · Cited by 216 · see citing works
Content Based Image Retrieval using Color and Texture
Singha, M. (2012) · Signal & Image Processing An International Journal · doi:10.5121/sipij.2012.3104 · Cited by 215 · see citing works
“A lot of research work has been carried out on Image Retrieval by many researchers, expanding in both depth and breadth [1]-[5].”
Visual diversification of image search results
van Leuken, R. H., Garcia, L., Olivares, X., & van Zwol, R. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526756 · Cited by 214 · see citing works
“Moving on from simple low-level features to more discriminative descriptions, the field has come a long way in narrowing down the semantic gap by using high-level semantics [8].”
Local Mesh Patterns Versus Local Binary Patterns: Biomedical Image Indexing and Retrieval
Murala, S., & Wu, Q. M. J. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2288522 · Cited by 213 · see citing works
Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking
Subrahmanyam, M. V. V. S., Maheshwari, R. P., & Raman, B. (2011) · Signal Processing · doi:10.1016/j.sigpro.2011.12.005 · Cited by 200 · see citing works
“Comprehensive and extensive literature survey on CBIR is presented in [1–4].”
BackgroundRECENT ADVANCES ON SUPPORT VECTOR MACHINES RESEARCH
Tian, Y., Shi, Y., & Liu, X. (2012) · Technological and Economic Development of Economy · doi:10.3846/20294913.2012.661205 · Cited by 196 · see citing works
“…in many fields ranging from text categorization (Joachims 1999a; Lodhi et al. Cao 2001), information and image retrieval (Druker et al. 2001; Liu et al. 2007; Tian et al. 2000), information security (Mukkamala et al. 2002) and etc. (Adankon, Cheriet 2009; An-cona et al. 2001; Azimi-Sadjadi,…”
BackgroundMethodFeature selection in image analysis: a survey
Bolón‐Canedo, V., & Remeseiro, B. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09750-3 · Cited by 193 · see citing works
“Two different frameworks can be found in the literature (Liu et al. 2007): one is content-based, i.”
BackgroundInfluentialMultichannel Decoded Local Binary Patterns for Content-Based Image Retrieval
Dubey, S. R., Singh, S. K., & Singh, R. K. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2577887 · Cited by 191 · see citing works
Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data
Bach, B., Shi, C., Heulot, N., Madhyastha, T. M., Grabowski, T., & Dragicevic, P. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2467851 · Cited by 185 · see citing works
“A wide range of far more elaborate approaches exist, from color histogram comparison [30, 31] to local feature matching [32].”
Remote Sensing Image Retrieval With Global Morphological Texture Descriptors
Aptoula, E. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2268736 · Cited by 181 · see citing works
A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm
Lai, C., & Chen, Y.-C. (2011) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2011.2135010 · Cited by 175 · see citing works
“There are some literatures that survey the most important CBIR systems [7], [8].”
BackgroundJoint embeddings of shapes and images via CNN image purification
Li, Y., Su, H., Qi, C. R., Fish, N., Cohen‐Or, D., & Guibas, L. J. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818071 · Cited by 174 · see citing works
“The difference between these two levels is referred to as the semantic gap [Smeulders et al. 2000], and is the main focus of CBIR methods, as surveyed in [Liu et al. 2007].”
BackgroundMethodDirectional local extrema patterns: a new descriptor for content based image retrieval
Murala, S., Maheshwari, R. P., & Raman, B. (2012) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-012-0008-2 · Cited by 171 · see citing works
Content-based image retrieval: A review of recent trends
Hameed, I. M., Abdulhussain, S. H., & Mahmmod, B. M. (2021) · Cogent Engineering · doi:10.1080/23311916.2021.1927469 · Cited by 167 · see citing works
Knowledge graph construction and application in geosciences: A review
Ma, X. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105082 · Cited by 174 · see citing works
Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases
Dubey, S. R., Singh, S. K., & Singh, R. K. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2493446 · Cited by 163 · see citing works
Recent Advance in Content-based Image Retrieval: A Literature SurveyPreprint
Zhou, W., Li, H., & Tian, Q. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06064 · Cited by 199 · see citing works
“The progress in those years has been comprehensively discussed in existing survey papers [5] [6] [7].”
BackgroundVerma, M., & Raman, B. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4834-3 · Cited by 153 · see citing works
“1 Motivation Many methods have been proposed for image feature extraction in content based image retrieval [23, 30, 47].”
BackgroundSemantic hierarchies for image annotation: A survey
Tousch, A.-M., Herbin, S., & Audibert, J.-Y. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.05.017 · Cited by 152 · see citing works
Region-based image retrieval with high-level semantics using decision tree learning
Liu, Y., Zhang, D., & Lu, G. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.12.003 · Cited by 150 · see citing works
“The state-of-the-art techniques for reducing the 'semantic gap' can be roughly classified into five categories [10].”
BackgroundDynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification
Wang, B., Zhuowen Tu, A., & Tsotsos, J. K. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.60 · Cited by 142 · see citing works
A Survey On: “Content Based Image Retrieval Systems”
Singhai, N., & Shandilya, S. K. (2010) · International Journal of Computer Applications · doi:10.5120/802-1139 · Cited by 134 · see citing works
Rashedi, E., Nezamabadi–pour, H., & Saryazdi, S. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.10.011 · Cited by 130 · see citing works
Local Bit-Plane Decoded Pattern: A Novel Feature Descriptor for Biomedical Image Retrieval
Dubey, S. R., Singh, S. K., & Singh, R. K. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2437396 · Cited by 126 · see citing works
Efficient manifold ranking for image retrieval
Xu, B., Bu, J., Chen, C., Cai, D., He, X., Liu, W., & Luo, J. (2011) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2009916.2009988 · Cited by 123 · see citing works
“COREL is widely used in many CBIR works [12, 19, 31].”
BackgroundLocal Diagonal Extrema Pattern: A New and Efficient Feature Descriptor for CT Image Retrieval
Dubey, S. R., Singh, S. K., & Singh, R. K. (2015) · IEEE Signal Processing Letters · doi:10.1109/lsp.2015.2392623 · Cited by 123 · see citing works
“The information contained in the image can be represented in the form of the feature vectors using efficient image descriptors which can be used in the content based image retrieval system to retrieve the most similar images of a given query image [3][4].”
BackgroundMethodA Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF
Ali, N., Bajwa, K. B., Sablatnig, R., Chatzichristofis, S. A., Iqbal, Z., Rashid, M., & Habib, H. A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157428 · Cited by 122 · see citing works
A survey and analysis on automatic image annotation
Cheng, Q., Zhang, Q., Fu, P., Tu, C., & Li, S. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.02.017 · Cited by 122 · see citing works
Content-based image retrieval using PSO and k-means clustering algorithm
Younus, Z. S., Mohamad, D., Saba, T., Alkawaz, M. H., Rehman, A. U., Al‐Rodhaan, M., & Al-Dhelaan, A. M. (2014) · Arabian Journal of Geosciences · doi:10.1007/s12517-014-1584-7 · Cited by 121 · see citing works
Malik, F., & Baharudin, B. (2012) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2012.11.004 · Cited by 117 · see citing works
“To overcome the problem of space and manipulation time, at present almost all images are represented in compressed formats like JPEG and MPEG (Liu et al., 2007; Nezamabadi-pour and Saryazdi, 2005).”
BackgroundMethodInfluentialWen, D., Huang, X., Zhang, L., & Benediktsson, J. A. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2463075 · Cited by 116 · see citing works
“, scene classification [47]–[49], content-based image retrieval [50], and complex”
BackgroundDirectional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval
Murala, S., Maheshwari, R. P., & Raman, B. (2011) · Journal of Medical Systems · doi:10.1007/s10916-011-9764-4 · Cited by 116 · see citing works
Siamese graph convolutional network for content based remote sensing image retrieval
Chaudhuri, U., Banerjee, B., & Bhattacharya, A. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.04.004 · Cited by 113 · see citing works
Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data
Belgiu, M., Tomljenović, I., Lampoltshammer, T. J., Blaschke, T., & Höfle, B. (2014) · Remote Sensing · doi:10.3390/rs6021347 · Cited by 114 · see citing works
“A comprehensive review of the role of ontology to content-based image retrieval and classification of VHR data can be found here [19,38].”
BackgroundComputer-Aided Diagnosis of Mammographic Masses Using Scalable Image Retrieval
Jiang, M., Zhang, S., Li, H., & Metaxas, D. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2365494 · Cited by 114 · see citing works
Sharif, U., Mehmood, Z., Mahmood, T., Javid, M. A., Rehman, A. U., & Saba, T. (2018) · Artificial Intelligence Review · doi:10.1007/s10462-018-9636-0 · Cited by 114 · see citing works
“In the last three decades, different feature extraction techniques have been employed based on appearances such as color, boundary contour, texture, and spatial layout (Mukherjee et al. 2015; Yuan et al. 2011; Liu et al. 2007; Zhang et al. 2012; Krajník et al. 2015; Arampatzis et al. 2013; Leutenegger et al. 2011; Low…”
BackgroundMethodInfluentialNaaman, M. (2010) · Multimedia Tools and Applications · doi:10.1007/s11042-010-0538-7 · Cited by 113 · see citing works
On Combining Multiple Features for Cartoon Character Retrieval and Clip Synthesis
Yu, J., Liu, D., Tao, D., & Seah, H. S. (2012) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2012.2192108 · Cited by 112 · see citing works
“Another research domain related to cartoon retrieval and synthesis is content-based image retrieval [20], [28], [29], [33].”
BackgroundSemantics for Robotic Mapping, Perception and Interaction: A Survey
Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., Wu, Q., Chin, T.-J., Reid, I. D., Gould, S. J., et al. (2020) · Foundations and Trends in Robotics · doi:10.1561/2300000059 · Cited by 114 · see citing works
SOS: A multimedia recommender System for Online Social networks
Amato, F., Moscato, V., Picariello, A., & Piccialli, F. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.04.028 · Cited by 101 · see citing works
Processing Social Media Messages in Mass Emergency: A SurveyPreprint
Imran, M., Castillo, C., Díaz, F., & Vieweg, S. E. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.7071 · Cited by 100 · see citing works
“In addition, there are cases of websites being set up in response to crises in 2003 [Palen and Liu 2007].”
BackgroundEMR: A Scalable Graph-Based Ranking Model for Content-Based Image Retrieval
Xu, B., Bu, J., Chen, C., Wang, C., Cai, D., & He, X. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.70 · Cited by 97 · see citing works
“COREL is widely used in many CBIR works [2], [41], [42].”
MethodFeature selection based-on genetic algorithm for image annotation
Lu, J., Zhao, T., & Zhang, Y. (2008) · Knowledge-Based Systems · doi:10.1016/j.knosys.2008.03.051 · Cited by 97 · see citing works
BOEW: A Content-Based Image Retrieval Scheme Using Bag-of-Encrypted-Words in Cloud Computing
Xia, Z., Jiang, L., Liu, D., Lu, L., & Jeon, B. (2019) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2019.2927215 · Cited by 96 · see citing works
Understanding Digital Image Processing
Tyagi, V. (2018) · CRC Press eBooks · doi:10.1201/9781315123905 · Cited by 92 · see citing works
Jabeen, S., Mehmood, Z., Mahmood, T., Saba, T., Rehman, A. U., & Mahmood, M. T. (2018) · PLoS ONE · doi:10.1371/journal.pone.0194526 · Cited by 90 · see citing works
Gao, J., & Long, C. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3654970 · Cited by 90 · see citing works
Assessment of Crohn’s Disease Lesions in Wireless Capsule Endoscopy Images
Kumar, R., Zhao, Q., Seshamani, S., Mullin, G. E., Hager, G. D., & Dassopoulos, T. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2172438 · Cited by 87 · see citing works
“In comparison to other content-based image retrieval [8] and conventional picture archiving and communication systems [9], only a few research systems [10]–[12] have investigated visual medical imaging such as conventional endoscopy, or CE images.”
BackgroundContent-based medical image classification using a new hierarchical merging scheme
Pourghassem, H., & Ghassemian, H. (2008) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2008.07.006 · Cited by 87 · see citing works
“To access images according to their content, low-level features such as colors, textures, and shapes of objects are widely used as indexing features for image retrieval [1].”
Attention-driven salient edge(s) and region(s) extraction with application to CBIR
Feng, S., Xu, D., & Yang, X. (2009) · Signal Processing · doi:10.1016/j.sigpro.2009.05.017 · Cited by 86 · see citing works
Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions
Ashraf, R., Bashir, K., Irtaza, A., & Mahmood, M. T. (2015) · Entropy · doi:10.3390/e17063552 · Cited by 85 · see citing works
“, distance or semantic association by a machine learning technique [15].”
BackgroundMethodRelevance feedback based on genetic programming for image retrieval
Ferreira, C. D., Santos, J. A., Torres, R. D. S., Gonçalves, M. A., Rezende, R. C., & Fan, W. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.05.015 · Cited by 85 · see citing works
“Motivated by these limitations, relevance feed-back (RF) approaches were incorporated into CBIR systems [31,26,5,27,13].”
MethodMehmood, Z., Mahmood, T., & Javid, M. A. (2017) · Applied Intelligence · doi:10.1007/s10489-017-0957-5 · Cited by 84 · see citing works
Real-time object detection and localization with SIFT-based clustering
Piccinini, P., Prati, A., & Cucchiara, R. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.06.004 · Cited by 84 · see citing works
“…June 2012 Keywords: Pick-and-place applications Machine vision for industrial applications SIFT This paper presents an innovative approach for detecting and localizing duplicate objects in pick-and-place applications under extreme conditions of occlusion, where standard appearance-based…”
BackgroundLearning image similarity from Flickr groups using Stochastic Intersection Kernel MAchines
Wang, G., Hoiem, D., & Forsyth, D. A. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459167 · Cited by 84 · see citing works
“The experimental results show our approach performs better on image matching, retrieval, and classification than using conventional visual features.”
MethodHavas, C., Resch, B., Francalanci, C., Pernici, B., Scalia, G., Fernandez-Marquez, J., Achte, T. V., Zeug, G., Mondardini, M., Grandoni, D., et al. (2017) · Italian National Conference on Sensors · doi:10.3390/s17122766 · Cited by 71 · see citing works
“Available algorithms can determine colour, shape and image texture [57].”
BackgroundXie, L., Wang, J., Zhang, B., & Tian, Q. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2408566 · Cited by 81 · see citing works
“A survey on this topic could be found in [68].”
BackgroundRETRACTED ARTICLE: Video analytics for semantic substance extraction using OpenCV in python
Manju, A., & Valarmathie, P. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01780-y · Cited by 81 · see citing works
RetractedNonlinear Deep Kernel Learning for Image Annotation
Jiu, M., & Sahbi, H. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2666038 · Cited by 80 · see citing works
“This framework is closely related to Laplacian SVMs, and its objective function is defined as min w,{ fk} K∑ k=1 Ck ∑ i=1 max ( 0, 1− yki fk(xi ) )+ 1 2 ∥ ∥ fk ∥ ∥2H +λ2 +u∑ i=1 ∑ j∈Ni A(xi , x j )( fk(xi)− fk(x j ))2.”
Medical Image Retrieval: Past and Present
Hwang, K. H., Lee, H., & Choi, D.-J. (2012) · Healthcare Informatics Research · doi:10.4258/hir.2012.18.1.3 · Cited by 77 · see citing works
Towards automated classification of fine-art painting style
Arora, R. (2012) · Rutgers University Community Repository (Rutgers University) · doi:10.7282/t3xp73qp · Cited by 82 · see citing works
“in [24] provides a comprehensive study of five major techniques utilizing semantic level features and their applications in CBIR systems.”
PhotoNet: A Similarity-Aware Picture Delivery Service for Situation Awareness
Uddin, M. Y. S., Wang, H., Saremi, F., Qi, G.-J., Abdelzaher, T., & Huang, T. S. (2011) · IEEE Real-Time Systems Symposium (RTSS) · doi:10.1109/rtss.2011.36 · Cited by 76 · see citing works
“We borrow mechanisms from vision literature [ 1 ] to determine visual similarity between communicated images.”
MethodMemon, M. H., Li, J., Memon, I., & Arain, Q. A. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3834-z · Cited by 75 · see citing works
“It observed that, they are going to recognise the objects like video, robot vision, mobile devices and electronics games by using the concept of matching two images, motion texture and much more in [32, 40, 64, 86].”
BackgroundGao, J., & Long, C. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3589282 · Cited by 76 · see citing works
“K nearest neighbor (KNN) search in the high-dimensional Euclidean vector space is a fundamental problem and has a wide range of applications in information retrieval [41], data mining [13] and recommendations [53].”
BackgroundA privacy-preserving content-based image retrieval method in cloud environment
Xu, Y., Gong, J., Xiong, L., Xu, Z., Wang, J., & Shi, Y.-Q. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.01.006 · Cited by 74 · see citing works
Li, R., Lu, J., Zhang, Y., & Zhao, T. (2009) · Knowledge-Based Systems · doi:10.1016/j.knosys.2009.11.020 · Cited by 71 · see citing works
An Overview of Perceptual Hashing
Farid, H. (2021) · Journal of Online Trust and Safety · doi:10.54501/jots.v1i1.24 · Cited by 70 · see citing works
“The many choices for each of these stages result in varying compromises between the five desired properties of a hash (see Smeulders et al. 2000; Liu et al. 2007; Du, Ho, and Cong 2020 for extensive surveys of different techniques).”
BackgroundRemote Sensing Image Retrieval by Scene Semantic Matching
Wang, M., & Song, T. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2217397 · Cited by 70 · see citing works
“SBIR/SBRSIR currently involves the following three main aspects [1], [10], [11]: 1) image semantic representation; 2) image semantic extraction for bridging image low-level VFs to high-level semantics; and 3) semantic matching and utilization.”
BackgroundThe Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval
Asim, M. N., Wasim, M., Khan, M. U. G., Mahmood, N., & Mahmood, W. (2019) · IEEE Access · doi:10.1109/access.2019.2897849 · Cited by 70 · see citing works
“fashion, medicine, publishing, architecture and remote sensing etc [83].”
BackgroundMethodAn Approach to Supporting Incremental Visual Data Classification
Paiva, J. G. S., Schwartz, W. R., Pedrini, H., & Minghim, R. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2331979 · Cited by 68 · see citing works
“Second, users tend to employ high-level concepts to interpret images and measure their similarity, while mostly low-level features are automatically extracted, producing a semantic gap [25].”
Segalin, C., Cheng, D., & Cristani, M. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.10.013 · Cited by 68 · see citing works
Image annotation: Then and now
Bhagat, P. K., & Choudhary, P. (2018) · Image and Vision Computing · doi:10.1016/j.imavis.2018.09.017 · Cited by 66 · see citing works
A generic framework for ontology-based information retrieval and image retrieval in web data
Vijayarajan, V., Dinakaran, M., Tejaswin, P., & Lohani, M. (2016) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-016-0074-1 · Cited by 66 · see citing works
Remote Sensing Image Retrieval in the Past Decade: Achievements, Challenges, and Future Directions
Zhou, W., Guan, H., Li, Z., Shao, Z., & Delavar, M. R. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3236662 · Cited by 64 · see citing works
“However, the results cannot well reflect the users’ real query intensions due to the “semantic gap” between the visual features and, in particular, the low-level features and the richness of human semantics [209].”
BackgroundDynamic label propagation for semi-supervised multi-class multi-label classification
Wang, B., & Tsotsos, J. K. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.10.006 · Cited by 65 · see citing works
Facial attributes for active authentication on mobile devices
Samangouei, P., Patel, V. M., & Chellappa, R. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.05.004 · Cited by 64 · see citing works
“The first use of attributes as higher order features was introduced in content-basedd image retrieval where they are presented as a solution to decrease the semantic gap [12, 13, 14].”
MethodA review of content-based image retrieval
Rafiee, G., Dlay, S. S., & Woo, W. L. (2010) · International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) · doi:10.1109/csndsp16145.2010.5580313 · Cited by 62 · see citing works
Non-Boolean computing with nanomagnets for computer vision applications
Bhanja, S., Karunaratne, D. K., Panchumarthy, R., Rajaram, S., & Sarkar, S. (2015) · Nature Nanotechnology · doi:10.1038/nnano.2015.245 · Cited by 62 · see citing works
Sarwar, A., Mehmood, Z., Saba, T., Qazi, K. A., Adnan, A., & Jamal, H. (2018) · Journal of Information Science · doi:10.1177/0165551518782825 · Cited by 62 · see citing works
Content-based medical image retrieval system for lung diseases using deep CNNs
Agrawal, S., Chowdhary, A., Agarwala, S., Mayya, V., & Kamath, S. S. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-01007-7 · Cited by 61 · see citing works
“The performance of such a CBIR system is highly reliant on chosen features [6], which may result in overfitting when the quantity of input images is inadequate.”
BackgroundJuneja, K., Verma, A., Goel, S., & Goel, S. (2015) · International Conference on Computational Intelligence & Communication Technology (CICT) · doi:10.1109/cict.2015.92 · Cited by 61 · see citing works
“For image retrieving purpose, two frameworks were developed, text-based image retrieval (TBIR) and content-based image retrieval (CBIR) [1].”
MethodImage Retrieval Based on Content Using Color Feature
Afifi, A. J., & Ashour, W. M. (2012) · ISRN Computer Graphics · doi:10.5402/2012/248285 · Cited by 61 · see citing works
“Recall is defined as the ratio of the number of retrieved relevant images to the total number of relevant images in the database [33].”
BackgroundCBIR USING COLOR HISTOGRAM PROCESSING
Suhasini, P. S., & Krishna, I. V. M. (2009) · Cited by 59 · see citing works
4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling
Bahmani, S., Skorokhodov, I., Rong, V., Wetzstein, G., Guibas, L. J., Wonka, P., Tulyakov, S., Park, J. J., Tagliasacchi, A., & Lindell, D. B. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00764 · Cited by 63 · see citing works
“The problem itself is also related to other methods for text-based image retrieval [35] or image-conditioned text generation [62, 78].”
Pedronette, D. C. G., Gonçalves, F. M. F., & Guilherme, I. R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.05.009 · Cited by 59 · see citing works
“Mainly due to this difficulties, the research focus was gradually shifted from designing low-level features to aspects related to higher level aspects [9].”
A Novel Image Retrieval Based on a Combination of Local and Global Histograms of Visual Words
Mehmood, Z., Anwar, S. M., Ali, N., Habib, H. A., & Rashid, M. (2016) · Mathematical Problems in Engineering · doi:10.1155/2016/8217250 · Cited by 58 · see citing works
“The main focus of the research in CBIR is to retrieve the images that are in a semantic relationship with the query image [3, 5].”
BackgroundGlobal Correlation Descriptor: A novel image representation for image retrieval
Feng, L., Wu, J., Liu, S., & Zhang, H. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.09.002 · Cited by 58 · see citing works
- Authors
- Liu, Y., Zhang, D., Lu, G., & Ma, W.-Y.
- Affiliations
- Monash University (AU)
- Microsoft Research Asia (China) (CN)
- Corresponding author
- Liu, Ying
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2007-01
- Volume
- 40
- Issue
- 1
- Pages
- 262-282
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2046589280
- Cited by
- 1,696
- References
- 156
- Citations by year
- 2026: 12 · 2025: 36 · 2024: 37 · 2023: 49 · 2022: 44 · 2021: 73 · 2020: 73 · 2019: 91 · 2018: 109 · 2017: 132 · 2016: 129 · 2015: 137 · 2014: 138 · 2013: 121 · 2012: 130
- FWCI
- 67.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- content-based image retrieval, high-level semantics, semantic gap, low-level feature extraction, similarity measurement, object ontology, relevance feedback, semantic templates, web image search, information retrieval evaluation
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